class_docstr: Second pass through fmri_image.
  
  Parameters
  ----------
  fmri_image : `FmriImageList`
     object returning 4D array from ``np.asarray``, having attribute
     ``volume_start_times`` (if `volume_start_times` is None), and
     such that ``object[0]`` returns something with attributes ``shape``
  formula :  :class:`nipy.algorithms.statistics.formula.Formula`
  rho : ``Image``
     image of AR(1) coefficients.  Returning data from
     ``rho.get_fdata()``, and having attribute ``coordmap``
  outputs : list
      Store for model outputs.
  volume_start_times: None or float or (N,) ndarray
      start time of each frame. It can be specified either as an ndarray
      with ``N=len(images)`` elements or as a single float, the TR. None
      results in ``np.arange(len(images)).astype(np.float64)``
  
  Raises
  ------
  ValueError
      If `volume_start_times` not specified, and 4D image passed.
code: def __init__(
      self,
      fmri_image,
      formula,
      rho,
      outputs=None,
      volume_start_times=None,
  ):
      super().__init__(fmri_image, formula, outputs, volume_start_times)
      # Cleanup rho values, truncate them to a scale of 0.01
      g = copy.copy(rho.coordmap)
      rho = rho.get_fdata()
      m = np.isnan(rho)
      r = (np.clip(rho, -1, 1) * 100).astype(np.int_) / 100.0
      r[m] = np.inf
      self.rho = Image(r, g)
  
imports: import copy
  from os import path
  import numpy
  import numpy.linalg
  from nipy.algorithms.statistics.formula import make_recarray
  from nipy.algorithms.statistics.models.regression import ARModel, OLSModel, ar_bias_correct, ar_bias_corrector
  from nipy.core.api import AffineTransform, Image, matrix_generator, parcels
  from nipy.io.api import save_image
  from ..api import FmriImageList, axis0_generator
  from .None import outputters
label_desc: Use image node_id 1 to create a new AR1 object from inherited base classes: OLS with example: obj = AR1(fmri_image, formula, rho, outputs, volume_start_times)